{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bad5f6dd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store_id</th>\n",
       "      <th>city</th>\n",
       "      <th>channel</th>\n",
       "      <th>gender_group</th>\n",
       "      <th>age_group</th>\n",
       "      <th>wkd_ind</th>\n",
       "      <th>product</th>\n",
       "      <th>customer</th>\n",
       "      <th>revenue</th>\n",
       "      <th>order</th>\n",
       "      <th>quant</th>\n",
       "      <th>unit_cost</th>\n",
       "      <th>unit_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>658</td>\n",
       "      <td>深圳</td>\n",
       "      <td>线下</td>\n",
       "      <td>Female</td>\n",
       "      <td>25-29</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>当季新品</td>\n",
       "      <td>4</td>\n",
       "      <td>796.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>59</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>146</td>\n",
       "      <td>杭州</td>\n",
       "      <td>线下</td>\n",
       "      <td>Female</td>\n",
       "      <td>25-29</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>运动</td>\n",
       "      <td>1</td>\n",
       "      <td>149.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>深圳</td>\n",
       "      <td>线下</td>\n",
       "      <td>Male</td>\n",
       "      <td>&gt;=60</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>T恤</td>\n",
       "      <td>2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>658</td>\n",
       "      <td>深圳</td>\n",
       "      <td>线下</td>\n",
       "      <td>Female</td>\n",
       "      <td>25-29</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>T恤</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>229</td>\n",
       "      <td>深圳</td>\n",
       "      <td>线下</td>\n",
       "      <td>Male</td>\n",
       "      <td>20-24</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>袜子</td>\n",
       "      <td>2</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22288</th>\n",
       "      <td>146</td>\n",
       "      <td>杭州</td>\n",
       "      <td>线下</td>\n",
       "      <td>Female</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>短裤</td>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22289</th>\n",
       "      <td>430</td>\n",
       "      <td>成都</td>\n",
       "      <td>线下</td>\n",
       "      <td>Female</td>\n",
       "      <td>25-29</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>T恤</td>\n",
       "      <td>1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22290</th>\n",
       "      <td>449</td>\n",
       "      <td>武汉</td>\n",
       "      <td>线下</td>\n",
       "      <td>Female</td>\n",
       "      <td>35-39</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>T恤</td>\n",
       "      <td>1</td>\n",
       "      <td>158.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22291</th>\n",
       "      <td>758</td>\n",
       "      <td>杭州</td>\n",
       "      <td>线下</td>\n",
       "      <td>Female</td>\n",
       "      <td>20-24</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>袜子</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22292</th>\n",
       "      <td>616</td>\n",
       "      <td>成都</td>\n",
       "      <td>线下</td>\n",
       "      <td>Male</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>当季新品</td>\n",
       "      <td>1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22293 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       store_id city channel gender_group age_group  wkd_ind product  \\\n",
       "0           658   深圳      线下       Female     25-29  Weekday    当季新品   \n",
       "1           146   杭州      线下       Female     25-29  Weekday      运动   \n",
       "2            70   深圳      线下         Male      >=60  Weekday      T恤   \n",
       "3           658   深圳      线下       Female     25-29  Weekday      T恤   \n",
       "4           229   深圳      线下         Male     20-24  Weekend      袜子   \n",
       "...         ...  ...     ...          ...       ...      ...     ...   \n",
       "22288       146   杭州      线下       Female     30-34  Weekday      短裤   \n",
       "22289       430   成都      线下       Female     25-29  Weekend      T恤   \n",
       "22290       449   武汉      线下       Female     35-39  Weekday      T恤   \n",
       "22291       758   杭州      线下       Female     20-24  Weekday      袜子   \n",
       "22292       616   成都      线下         Male     30-34  Weekday    当季新品   \n",
       "\n",
       "       customer  revenue  order  quant  unit_cost  unit_price  \n",
       "0             4    796.0      4      4         59         199  \n",
       "1             1    149.0      1      1         49         149  \n",
       "2             2    178.0      2      2         49          89  \n",
       "3             1     59.0      1      1         49          59  \n",
       "4             2     65.0      2      3          9          22  \n",
       "...         ...      ...    ...    ...        ...         ...  \n",
       "22288         1     80.0      1      2         19          40  \n",
       "22289         1     79.0      1      1         49          79  \n",
       "22290         1    158.0      1      2         49          79  \n",
       "22291         1     26.0      1      1          9          26  \n",
       "22292         1     79.0      1      1         59          79  \n",
       "\n",
       "[22293 rows x 13 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "#读取商品购买信息\n",
    "data1 = pd.read_csv('D:桌面/uniqlo.csv',encoding='utf-8')\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "38c3e867",
   "metadata": {},
   "outputs": [],
   "source": [
    "##数据清洗：信息表去重\n",
    "#print('信息表去重前表的形状为：',data1.shape)\n",
    "#data2 = data1.drop_duplicates(subset=None,keep='first',inplace=False)\n",
    "#print('信息表去重后表的形状为：',data2.shape)\n",
    "#data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "42696b30",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store_id</th>\n",
       "      <th>city</th>\n",
       "      <th>channel</th>\n",
       "      <th>gender_group</th>\n",
       "      <th>age_group</th>\n",
       "      <th>wkd_ind</th>\n",
       "      <th>product</th>\n",
       "      <th>customer</th>\n",
       "      <th>revenue</th>\n",
       "      <th>order</th>\n",
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       "      <th>unit_cost</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>245</td>\n",
       "      <td>杭州</td>\n",
       "      <td>线下</td>\n",
       "      <td>Female</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>当季新品</td>\n",
       "      <td>1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>335</td>\n",
       "      <td>上海</td>\n",
       "      <td>线下</td>\n",
       "      <td>Female</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>T恤</td>\n",
       "      <td>1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>315</td>\n",
       "      <td>武汉</td>\n",
       "      <td>线下</td>\n",
       "      <td>Female</td>\n",
       "      <td>25-29</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>T恤</td>\n",
       "      <td>1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>802</td>\n",
       "      <td>西安</td>\n",
       "      <td>线下</td>\n",
       "      <td>Male</td>\n",
       "      <td>&gt;=60</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>T恤</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>810</th>\n",
       "      <td>325</td>\n",
       "      <td>上海</td>\n",
       "      <td>线下</td>\n",
       "      <td>Female</td>\n",
       "      <td>25-29</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>牛仔裤</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22277</th>\n",
       "      <td>611</td>\n",
       "      <td>深圳</td>\n",
       "      <td>线下</td>\n",
       "      <td>Male</td>\n",
       "      <td>20-24</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>T恤</td>\n",
       "      <td>1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22278</th>\n",
       "      <td>70</td>\n",
       "      <td>深圳</td>\n",
       "      <td>线下</td>\n",
       "      <td>Male</td>\n",
       "      <td>&gt;=60</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>短裤</td>\n",
       "      <td>1</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22280</th>\n",
       "      <td>21</td>\n",
       "      <td>北京</td>\n",
       "      <td>线下</td>\n",
       "      <td>Female</td>\n",
       "      <td>35-39</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>T恤</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22289</th>\n",
       "      <td>430</td>\n",
       "      <td>成都</td>\n",
       "      <td>线下</td>\n",
       "      <td>Female</td>\n",
       "      <td>25-29</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>T恤</td>\n",
       "      <td>1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22290</th>\n",
       "      <td>449</td>\n",
       "      <td>武汉</td>\n",
       "      <td>线下</td>\n",
       "      <td>Female</td>\n",
       "      <td>35-39</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>T恤</td>\n",
       "      <td>1</td>\n",
       "      <td>158.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3208 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       store_id city channel gender_group age_group  wkd_ind product  \\\n",
       "217         245   杭州      线下       Female     30-34  Weekday    当季新品   \n",
       "518         335   上海      线下       Female     30-34  Weekend      T恤   \n",
       "574         315   武汉      线下       Female     25-29  Weekend      T恤   \n",
       "669         802   西安      线下         Male      >=60  Weekday      T恤   \n",
       "810         325   上海      线下       Female     25-29  Weekday     牛仔裤   \n",
       "...         ...  ...     ...          ...       ...      ...     ...   \n",
       "22277       611   深圳      线下         Male     20-24  Weekday      T恤   \n",
       "22278        70   深圳      线下         Male      >=60  Weekend      短裤   \n",
       "22280        21   北京      线下       Female     35-39  Weekend      T恤   \n",
       "22289       430   成都      线下       Female     25-29  Weekend      T恤   \n",
       "22290       449   武汉      线下       Female     35-39  Weekday      T恤   \n",
       "\n",
       "       customer  revenue  order  quant  unit_cost  unit_price  \n",
       "217           1     79.0      1      1         59          79  \n",
       "518           1     79.0      1      1         49          79  \n",
       "574           1     79.0      1      1         49          79  \n",
       "669           1     59.0      1      1         49          59  \n",
       "810           1     59.0      1      1         69          59  \n",
       "...         ...      ...    ...    ...        ...         ...  \n",
       "22277         1     79.0      1      1         49          79  \n",
       "22278         1     40.0      1      1         19          40  \n",
       "22280         1     59.0      1      1         49          59  \n",
       "22289         1     79.0      1      1         49          79  \n",
       "22290         1    158.0      1      2         49          79  \n",
       "\n",
       "[3208 rows x 13 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#打印重复值\n",
    "df=data1[data1.duplicated()]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8222d8b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data2每个特征缺失的数目为：\n",
      " store_id        0\n",
      "city            0\n",
      "channel         0\n",
      "gender_group    0\n",
      "age_group       0\n",
      "wkd_ind         0\n",
      "product         0\n",
      "customer        0\n",
      "revenue         0\n",
      "order           0\n",
      "quant           0\n",
      "unit_cost       0\n",
      "unit_price      0\n",
      "dtype: int64\n",
      "data2每个特征非缺失的数目为：\n",
      " store_id        22293\n",
      "city            22293\n",
      "channel         22293\n",
      "gender_group    22293\n",
      "age_group       22293\n",
      "wkd_ind         22293\n",
      "product         22293\n",
      "customer        22293\n",
      "revenue         22293\n",
      "order           22293\n",
      "quant           22293\n",
      "unit_cost       22293\n",
      "unit_price      22293\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "##数据清洗：检测缺失值\n",
    "print('data2每个特征缺失的数目为：\\n',data1.isnull().sum())\n",
    "print('data2每个特征非缺失的数目为：\\n',data1.notnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8f09f395",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>channel</th>\n",
       "      <th>gender_group</th>\n",
       "      <th>age_group</th>\n",
       "      <th>wkd_ind</th>\n",
       "      <th>product</th>\n",
       "      <th>customer</th>\n",
       "      <th>revenue</th>\n",
       "      <th>order</th>\n",
       "      <th>quant</th>\n",
       "      <th>unit_cost</th>\n",
       "      <th>unit_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>深圳</td>\n",
       "      <td>线下</td>\n",
       "      <td>Female</td>\n",
       "      <td>25-29</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>当季新品</td>\n",
       "      <td>4</td>\n",
       "      <td>796.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>59</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>杭州</td>\n",
       "      <td>线下</td>\n",
       "      <td>Female</td>\n",
       "      <td>25-29</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>运动</td>\n",
       "      <td>1</td>\n",
       "      <td>149.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>深圳</td>\n",
       "      <td>线下</td>\n",
       "      <td>Male</td>\n",
       "      <td>&gt;=60</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>T恤</td>\n",
       "      <td>2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>深圳</td>\n",
       "      <td>线下</td>\n",
       "      <td>Female</td>\n",
       "      <td>25-29</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>T恤</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>深圳</td>\n",
       "      <td>线下</td>\n",
       "      <td>Male</td>\n",
       "      <td>20-24</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>袜子</td>\n",
       "      <td>2</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22288</th>\n",
       "      <td>杭州</td>\n",
       "      <td>线下</td>\n",
       "      <td>Female</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>短裤</td>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22289</th>\n",
       "      <td>成都</td>\n",
       "      <td>线下</td>\n",
       "      <td>Female</td>\n",
       "      <td>25-29</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>T恤</td>\n",
       "      <td>1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22290</th>\n",
       "      <td>武汉</td>\n",
       "      <td>线下</td>\n",
       "      <td>Female</td>\n",
       "      <td>35-39</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>T恤</td>\n",
       "      <td>1</td>\n",
       "      <td>158.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22291</th>\n",
       "      <td>杭州</td>\n",
       "      <td>线下</td>\n",
       "      <td>Female</td>\n",
       "      <td>20-24</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>袜子</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22292</th>\n",
       "      <td>成都</td>\n",
       "      <td>线下</td>\n",
       "      <td>Male</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>当季新品</td>\n",
       "      <td>1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22293 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      city channel gender_group age_group  wkd_ind product  customer  revenue  \\\n",
       "0       深圳      线下       Female     25-29  Weekday    当季新品         4    796.0   \n",
       "1       杭州      线下       Female     25-29  Weekday      运动         1    149.0   \n",
       "2       深圳      线下         Male      >=60  Weekday      T恤         2    178.0   \n",
       "3       深圳      线下       Female     25-29  Weekday      T恤         1     59.0   \n",
       "4       深圳      线下         Male     20-24  Weekend      袜子         2     65.0   \n",
       "...    ...     ...          ...       ...      ...     ...       ...      ...   \n",
       "22288   杭州      线下       Female     30-34  Weekday      短裤         1     80.0   \n",
       "22289   成都      线下       Female     25-29  Weekend      T恤         1     79.0   \n",
       "22290   武汉      线下       Female     35-39  Weekday      T恤         1    158.0   \n",
       "22291   杭州      线下       Female     20-24  Weekday      袜子         1     26.0   \n",
       "22292   成都      线下         Male     30-34  Weekday    当季新品         1     79.0   \n",
       "\n",
       "       order  quant  unit_cost  unit_price  \n",
       "0          4      4         59         199  \n",
       "1          1      1         49         149  \n",
       "2          2      2         49          89  \n",
       "3          1      1         49          59  \n",
       "4          2      3          9          22  \n",
       "...      ...    ...        ...         ...  \n",
       "22288      1      2         19          40  \n",
       "22289      1      1         49          79  \n",
       "22290      1      2         49          79  \n",
       "22291      1      1          9          26  \n",
       "22292      1      1         59          79  \n",
       "\n",
       "[22293 rows x 12 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据清洗：删除无意义列（store_id为门店随机编号id，无实际意义）\n",
    "data1.drop(['store_id'], axis=1, inplace=True) \n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4e0a07e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "线下    18403\n",
       "线上     3890\n",
       "Name: channel, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['channel'].unique()\n",
    "data1['channel'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3250a5f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    14208\n",
       "Male       7967\n",
       "Unkown      118\n",
       "Name: gender_group, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['gender_group'].unique()\n",
    "data1['gender_group'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "efeaf340",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30-34     4426\n",
       "25-29     4224\n",
       "35-39     3691\n",
       "20-24     3345\n",
       "40-44     1955\n",
       ">=60      1574\n",
       "45-49     1095\n",
       "50-54      672\n",
       "<20        660\n",
       "55-59      514\n",
       "Unkown     137\n",
       "Name: age_group, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['age_group'].unique()\n",
    "data1['age_group'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5cbefe8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>channel</th>\n",
       "      <th>gender_group</th>\n",
       "      <th>age_group</th>\n",
       "      <th>wkd_ind</th>\n",
       "      <th>product</th>\n",
       "      <th>customer</th>\n",
       "      <th>revenue</th>\n",
       "      <th>order</th>\n",
       "      <th>quant</th>\n",
       "      <th>unit_cost</th>\n",
       "      <th>unit_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>深圳</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25-29</td>\n",
       "      <td>0</td>\n",
       "      <td>当季新品</td>\n",
       "      <td>4</td>\n",
       "      <td>796.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>59</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>杭州</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25-29</td>\n",
       "      <td>0</td>\n",
       "      <td>运动</td>\n",
       "      <td>1</td>\n",
       "      <td>149.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>深圳</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>&gt;=60</td>\n",
       "      <td>0</td>\n",
       "      <td>T恤</td>\n",
       "      <td>2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>深圳</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25-29</td>\n",
       "      <td>0</td>\n",
       "      <td>T恤</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>深圳</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20-24</td>\n",
       "      <td>1</td>\n",
       "      <td>袜子</td>\n",
       "      <td>2</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22288</th>\n",
       "      <td>杭州</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30-34</td>\n",
       "      <td>0</td>\n",
       "      <td>短裤</td>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22289</th>\n",
       "      <td>成都</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25-29</td>\n",
       "      <td>1</td>\n",
       "      <td>T恤</td>\n",
       "      <td>1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22290</th>\n",
       "      <td>武汉</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35-39</td>\n",
       "      <td>0</td>\n",
       "      <td>T恤</td>\n",
       "      <td>1</td>\n",
       "      <td>158.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22291</th>\n",
       "      <td>杭州</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20-24</td>\n",
       "      <td>0</td>\n",
       "      <td>袜子</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22292</th>\n",
       "      <td>成都</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30-34</td>\n",
       "      <td>0</td>\n",
       "      <td>当季新品</td>\n",
       "      <td>1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22293 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      city channel gender_group age_group wkd_ind product  customer  revenue  \\\n",
       "0       深圳       1            0     25-29       0    当季新品         4    796.0   \n",
       "1       杭州       1            0     25-29       0      运动         1    149.0   \n",
       "2       深圳       1            1      >=60       0      T恤         2    178.0   \n",
       "3       深圳       1            0     25-29       0      T恤         1     59.0   \n",
       "4       深圳       1            1     20-24       1      袜子         2     65.0   \n",
       "...    ...     ...          ...       ...     ...     ...       ...      ...   \n",
       "22288   杭州       1            0     30-34       0      短裤         1     80.0   \n",
       "22289   成都       1            0     25-29       1      T恤         1     79.0   \n",
       "22290   武汉       1            0     35-39       0      T恤         1    158.0   \n",
       "22291   杭州       1            0     20-24       0      袜子         1     26.0   \n",
       "22292   成都       1            1     30-34       0    当季新品         1     79.0   \n",
       "\n",
       "       order  quant  unit_cost  unit_price  \n",
       "0          4      4         59         199  \n",
       "1          1      1         49         149  \n",
       "2          2      2         49          89  \n",
       "3          1      1         49          59  \n",
       "4          2      3          9          22  \n",
       "...      ...    ...        ...         ...  \n",
       "22288      1      2         19          40  \n",
       "22289      1      1         49          79  \n",
       "22290      1      2         49          79  \n",
       "22291      1      1          9          26  \n",
       "22292      1      1         59          79  \n",
       "\n",
       "[22293 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.replace('Weekday','0', inplace=True)\n",
    "data1.replace('Weekend','1', inplace=True)\n",
    "data1.replace('线上','0', inplace=True)\n",
    "data1.replace('线下','1', inplace=True)\n",
    "data1.replace('Female','0', inplace=True)\n",
    "data1.replace('Male','1', inplace=True)\n",
    "data1.replace('Unknow','2', inplace=True)\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "04b1fa9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='wkd_ind', ylabel='revenue'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpmXehkOSoJNcnuTHJqX33I0mTZF6FQpI9gH8CjgYOBZYlObTfriRpcsyrUACOAG6sqpur6ufAx4Fjeu5JkibGfAuFJwLfH3i9pqtJksZgQd8NbCOz1OoBGyTLgeXdyx8nuX7kXU2OhcAP+m5iPsi7ju+7BT2Qv5tbnT7bn8md9uTtrZhvobAGeNLA6wOAOwY3qKoVwIpxNjUpkqyqqum++5C25e/m+My34aOvAwcnOTDJo4DjgIt67kmSJsa8OlOoqs1JXg98HtgDOKeqruu5LUmaGPMqFACq6mLg4r77mFAOy2m+8ndzTFJVO95KkjQR5ttnCpKkHhkKcmoRzVtJzkmyIcm1ffcyKQyFCefUIprnzgWO6ruJSWIoyKlFNG9V1eXApr77mCSGgpxaRFJjKGiHU4tImhyGgnY4tYikyWEoyKlFJDWGwoSrqs3A1qlFvguc79Qimi+SrASuBA5JsibJ6/ruaXfnHc2SpMYzBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUpCEl+fEQ25yb5I8eZP0/7+wstMMcV5or8+7rOKXdWVX9ad89SA/GMwWpk+SUJH/RLb8nyZe65ecn+cjAdguTXJnk9zLjvUm+k+RzwL47OMaXk0x3yz9OckaSbyW5Ksl+Xf3A7v2/nuTvRvYDS7MwFKT7XQ48u1ueBh6X5JHAs4ArALo/3J8DTquqzwHHAocATwNOAH53J463J3BVVT2jO/YJXf0fgbOr6neA9Q/pJ5J2kqEg3W81cHiSvYB7mZlzZ5qZoLgCeCRwKXBKVV3S7fMcYGVVbamqO4Av7cTxfg58duDYS7rlZwIru+V/2bUfRdo1hoLUqapfALcCrwG+ykwQHAkcxMxkgZuZ+eP94m133cVD/qLun3xsCw/8jM9JydQLQ0F6oMuBN3XPVwAnAVd3f7wLeC3wG0lOHdj+uCR7JFnETIg8VP/FzBTmAK+cg/eThmYoSA90BbAIuLKq7gT+r6sBUFVbmPmDfWSSPwcuBG4Avg2cDVw2Bz28ATg5ydeBX5uD95OG5tTZkqTGMwVJUuPNa9IIJLkQOHCb8luq6vN99CMNy+EjSVLj8JEkqTEUJEmNoSBJagwFSVJjKEiSmv8HGcRW7ckBLNcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##数据分析\n",
    "##销售额随时间的变化\n",
    "plt.figure(figsize=(6,5))\n",
    "sns.barplot(x='wkd_ind',y='revenue',data=data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0c8ec937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wkd_ind</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12465.0</td>\n",
       "      <td>167.986200</td>\n",
       "      <td>310.773434</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>66.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>12538.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9828.0</td>\n",
       "      <td>148.807984</td>\n",
       "      <td>224.537169</td>\n",
       "      <td>0.00</td>\n",
       "      <td>59.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>7919.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count        mean         std   min   25%   50%    75%      max\n",
       "wkd_ind                                                                   \n",
       "0        12465.0  167.986200  310.773434 -0.66  66.0  99.0  192.0  12538.0\n",
       "1         9828.0  148.807984  224.537169  0.00  59.0  99.0  158.0   7919.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.groupby('wkd_ind').revenue.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "23f0546c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T恤</th>\n",
       "      <td>10610.0</td>\n",
       "      <td>145.027789</td>\n",
       "      <td>154.278714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>79.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>6636.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>当季新品</th>\n",
       "      <td>2540.0</td>\n",
       "      <td>232.545228</td>\n",
       "      <td>597.253282</td>\n",
       "      <td>0.00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>12538.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>毛衣</th>\n",
       "      <td>807.0</td>\n",
       "      <td>304.375217</td>\n",
       "      <td>290.733202</td>\n",
       "      <td>0.00</td>\n",
       "      <td>149.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>4975.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>牛仔裤</th>\n",
       "      <td>1412.0</td>\n",
       "      <td>174.311246</td>\n",
       "      <td>238.681718</td>\n",
       "      <td>0.00</td>\n",
       "      <td>59.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>2087.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>短裤</th>\n",
       "      <td>1694.0</td>\n",
       "      <td>63.450933</td>\n",
       "      <td>55.646467</td>\n",
       "      <td>0.00</td>\n",
       "      <td>37.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>676.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>袜子</th>\n",
       "      <td>2053.0</td>\n",
       "      <td>62.216931</td>\n",
       "      <td>51.183226</td>\n",
       "      <td>0.00</td>\n",
       "      <td>27.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>595.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>裙子</th>\n",
       "      <td>629.0</td>\n",
       "      <td>218.287409</td>\n",
       "      <td>172.449212</td>\n",
       "      <td>10.00</td>\n",
       "      <td>99.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>237.0</td>\n",
       "      <td>1442.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>运动</th>\n",
       "      <td>976.0</td>\n",
       "      <td>120.962787</td>\n",
       "      <td>142.740403</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>39.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>1257.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>配件</th>\n",
       "      <td>1572.0</td>\n",
       "      <td>282.878594</td>\n",
       "      <td>398.705054</td>\n",
       "      <td>0.00</td>\n",
       "      <td>99.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>4187.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count        mean         std    min    25%    50%    75%       max\n",
       "product                                                                       \n",
       "T恤       10610.0  145.027789  154.278714   0.00   79.0   99.0  158.0   6636.00\n",
       "当季新品      2540.0  232.545228  597.253282   0.00   76.0  111.0  197.0  12538.00\n",
       "毛衣         807.0  304.375217  290.733202   0.00  149.0  199.0  396.0   4975.00\n",
       "牛仔裤       1412.0  174.311246  238.681718   0.00   59.0   79.0  199.0   2087.00\n",
       "短裤        1694.0   63.450933   55.646467   0.00   37.0   40.0   77.0    676.00\n",
       "袜子        2053.0   62.216931   51.183226   0.00   27.0   52.0   79.0    595.36\n",
       "裙子         629.0  218.287409  172.449212  10.00   99.0  197.0  237.0   1442.00\n",
       "运动         976.0  120.962787  142.740403  -0.66   39.0   78.0  149.0   1257.00\n",
       "配件        1572.0  282.878594  398.705054   0.00   99.0  149.0  298.0   4187.00"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.groupby(['product']).revenue.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "38b7469b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='product', ylabel='revenue'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,5))\n",
    "from pylab import mpl  \n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei'] \n",
    "sns.barplot(x='product',y='revenue',data=data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e11af799",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='product', ylabel='revenue'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 按照revenue给分类排序\n",
    "data1.groupby(['product']).revenue.mean().sort_values(ascending = False).index\n",
    "plt.figure(figsize=(6,5))\n",
    "sns.barplot(x='product',y='revenue',data = data1,\n",
    "           order = data1.groupby(['product']).revenue.mean().sort_values(ascending = False).index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e3731454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='city', ylabel='revenue'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 不同城市的顾客，线上、线下方式的对比\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.barplot(x='city',y= 'revenue',hue ='channel',data = data1,\n",
    "           estimator = sum,\n",
    "           order =data1.groupby('city').revenue.sum().sort_values(ascending=False).index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "56fc2411",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gender_group', ylabel='revenue'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe4AAAF6CAYAAADf4LsDAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAWWUlEQVR4nO3df7DddZ3f8de7JBhNDPJrYQVZoKYo8mOVLBtmgAZHXHXs6gJVa2tk60i3s+OM01nTaWUo6zLYwZ2d3XFUJguLwnZRt6XWtYODVu4QRlACW5AqFGuRzZWL4WckhZUfn/5xTySGe5MTcs89+dz7eMxk7rknn/M97zh3fPL9nu/9fqu1FgCgD/9g3AMAAMMTbgDoiHADQEeEGwA6ItwA0BHhBoCOdBXuqjqsqjYOse6zVfVP5mMmAJhP3YS7qg5M8oUky3ez7owkh7fW/mZeBgOAedRNuJM8l+S9SbYmSVW9oqr+c1XdVFWfGTy3NMmfJ7m/qt41vlEBYDS6CXdrbWtr7Ykdnrogyd2ttTOT/GpVnZRkXZLvJ7ksyalV9ZExjAoAI9NNuGdwXJLfqaqJJMcmOSLJG5NsaK1NJfnLJGeNbzwAmHtLxj3AXrg3yXdba1dV1TuTPJDkh5mOeJKsTvLjcQ0HAKNQvd1kpKomWmtrq2p5kquSHJ7pz73fn6Ql+YskhyVZmuS81trk2IYFgDnWXbgBYDHr+TNuAFh0hBsAOtLFyWmHHHJIO/roo8c9BgDMm9tvv/3h1tqhOz/fRbiPPvrobNq0adxjAMC8qaoZfzPKoXIA6IhwA0BHhBsAOtLFZ9wAsKeeeeaZbN68OU8//fS4R9mlZcuW5cgjj8zSpUuHWi/cACxImzdvzitf+cocffTRqapxjzOj1loeeeSRbN68Occcc8xQr3GoHIAF6emnn87BBx+8z0Y7SaoqBx988B4dFRBuABasfTna2+3pjMINwKJ08cUXZ2JiYl7ea+3atXO2LeEGgI44OQ2AReHpp5/O+eefn82bN+dVr3pVjj/++HzjG9/IRRddlK1bt+brX/96VqxYkfPOOy/btm3La1/72lx11VW5+OKL88wzz2Tjxo2/WHf55Ze/6LmVK1dm3bp1+elPf5oTTzwxn/nMZ0by77DHDcCisGHDhpx88sm5+eabc+655+buu+/OD3/4w9x0000555xz8q1vfSsPPvhgPvKRj+Sb3/xm7r///jz00ENJ8qJ1Mz23YcOGnHDCCbnpppvy4IMP5q677hrJv8MeNwCLwj333JNzzz03SXL++efn/vvvz6mnnpokOeqoo/Lzn/88S5cuzRVXXJGrrroqjz76aJ566qkkybp1635p3UzP3Xvvvfn2t7+diYmJPP7445mcnMxJJ5005/8Oe9wjsH79+qxbty7r168f9ygADLzuda/LbbfdliS59NJLc8UVV2T58uW/tObKK6/Meeedl2uvvfaX/m7ndTM9d9xxx+WjH/1oJiYmcskll+Soo44awb9CuEdiamoqk5OTmZqaGvcoAAx8+MMfzh133JG1a9fmjjvuyAc+8IEXrTn77LPzyU9+Mm9+85uTJJOTk3u0/euvvz5nnnlmLr/88rzmNa+Zs9l3VK21kWx4Lq1evbr1dFvPdevWZXJyMkcccUSuvvrqcY8DsCj94Ac/yOtf//pxjzGUmWatqttba6t3XmuPGwA6ItwA0BHhBoCOCDcAdES4AaAjLsACwKJxysfm9jd9bv/Uut2ueeihh3Leeedl48aNc/Ke9rgBYEQee+yxfPCDH8y2bdvmbJvCDQAjst9+++VLX/pSVq5cOWfbdKgcAEZkLoO93YIP91x/njGMVz78s+yX5IGHfzbv7z/M5y0A9MuhcgDoiHADQEcW/KFyANhuXB8nTkxMzNm27HEDQEeEGwA6ItwA0BHhBoCOzHm4q+qAqrq+qm6oqv9aVfvPsu7Kqrqlqi6c6xkAYKEaxVnl/zzJn7TWvlFVn0vytiRf3XFBVZ2TZL/W2mlV9RdVtaq1dt8IZgGAX3jgEyfO6faOuuh7c7q9Ycz5Hndr7bOttW8Mvj00yU9nWLY2yZcHj29IcvpczwEA+4IPfehDOe2003LJJZfMyfZG9hl3VZ2W5MDW2q0z/PXyJJODx48mOWyG119QVZuqatOWLVtGNeZIPL//8jz3spV5fv/l4x4FgDG67rrr8txzz+WWW27Jj370o9x3394fXB7JBViq6qAkn05y7ixLnkzy8sHjFZnhPyBaaxuSbEiS1atXtxGMOTLbVr113CMAsA+YmJjIe97zniTJW9/61tx8881ZtWrVXm1zFCen7Z/kr5P8u9baj2dZdnteODx+cpL753oOABi3bdu25YgjjkiSHHTQQXnooYf2epuj2OP+UJI3Jfl4VX08yY1JlrbWdjx7/CtJNlbVq5O8PcmaEcwBAGO1YsWKPPXUU0mSJ598Ms8///xeb3POw91a+1ySz+1mzdaqWpvk7CSXtdaemOs5AGDcTjnllNx8881Zs2ZN7rzzzhx33HF7vc2x3WSktfZYXjizHABGbr5/fevd7353zjjjjPzkJz/J9ddfn1tvnel87T3jymkAMCIrV67MxMRE1qxZkxtvvDEHHHDAXm/TbT0BYIQOPPDAX5xZPhfscQOwYLW27/828Z7OKNwALEjLli3LI488sk/Hu7WWRx55JMuWLRv6NQ6VA7AgHXnkkdm8eXP29atvLlu2LEceeeTQ64UbgAVp6dKlOeaYY8Y9xpxzqBwAOiLcANAR4QaAjgg3AHREuAGgI8INAB0RbgDoiHADQEeEGwA6ItwA0BHhBoCOCDcAdES4AaAjwg0AHRFuAOiIcANAR4QbADoi3ADQEeEGgI4INwB0RLgBoCPCDQAdEW4A6IhwA0BHhBsAOiLcANAR4QaAjgg3AHREuAGgI8INAB0RbgDoiHADQEeEGwA6ItwA0BHhBoCOCDcAdES4AaAjwg0AHRFuAOiIcANAR4QbADoi3ADQEeEGgI4INwB0RLgBoCPCDQAdEW4A6IhwA0BHhBsAOiLcANAR4QaAjgg3AHREuAGgI8INAB0RbgDoiHADQEeEGwA6MpJwV9VhVbVxF3+/pKoeqKqJwZ8TRzEHACw0S+Z6g1V1YJIvJFm+i2UnJbm2tfZv5/r9AWAhG8Ue93NJ3ptk6y7WrEnyzqr6blVdWVVz/h8QALAQzXm4W2tbW2tP7GbZbUne0lo7NcnSJO/YeUFVXVBVm6pq05YtW+Z6TADo0rhOTrurtfbg4PGmJKt2XtBa29BaW91aW33ooYfO73QAsI8aV7ivqaqTq2q/JO9OcueY5gCArow83FV1fFVdstPTn0hyTZL/meSW1to3Rz0HACwEIzsprLW2dvD1+0ku3Onv7s70meUAwB5wARYA6IhwA0BHhBsAOiLcANAR4QaAjgg3AHREuAGgI8INAB0RbgDoiHADQEeEGwA6ItwA0BHhBoCOCDcAdES4AaAjwg0AHRFuAOiIcANAR4QbADoi3ADQEeEGgI4INwB0RLgBoCPCDQAdEW4A6IhwA0BHhBsAOiLcANAR4QaAjgg3AHREuAGgI8INAB0RbgDoiHADQEeWDLuwqk5IckSSB5L8XWvtyZFNBQDMaKg97qr6dJI/TPLJJMcm+atRDgUAzGzYQ+UnttbOTfJ4a+2/JzlghDMBALMYNtxbquqiJAdW1QeTTI1wJgBgFsOGe12SJ5Lckum97fNHNRAAMLthw/1PkzyW5DtJHh98DwDMs2HDXYM/L09yTpIzRzYRADCroX4drLX2hR2+vbyqPjuieQCAXRgq3FW14x72ryR5w2jGAQB2ZdgLsJyVpGX6cPnfJ/nXI5sIAJjVsJ9xX57kviT3J3kwyepRDQQAzG7YcF+f5B/u8H2NYBYAYDeGPVT+s9baJSOdBADYrWHDvbGqrk1ydZJtSdJau2lkUwEAMxo23M8kuSfJqYPvWxLhBoB5Nuzvcf/hzrf1HOlUAMCM3NYTADritp4A0BG39QSAjrzU23r+7sgmAgBmNexZ5e9IsqG19tQohwEAdm3YcK9K8l+q6rEkX03ytdbattGNBQDMZKhD5a21/9hae0eS30vyj5L8eKRTAQAzGva2nr+d5O1JjkxyW5IzRjkUADCzYQ+Vn5DkT1pr941yGABg14Y9VH5pkpdV1W9V1eurasWI5wIAZuDKaQDQEVdOA4COuHIaAHTEldMAoCPD3tbzqSR/NuJZAIDdGPbktOv3ZKNVdVhVbdzNmiur6paqunBPtg0Ai9mwh8q/V1XvGmZhVR2Y5AtJlu9izTlJ9mutnZbk2KpaNeQcALCoDRvu30jyxar6blXdWFXf2sXa55K8N8nWXaxZm+TLg8c3JDl95wVVdUFVbaqqTVu2bBlyTABY2Ib9jPusYTfYWtuaJFW1q2XLk0wOHj+a5E0zbGdDkg1Jsnr16jbs+wPAQjbsHvdcezLJywePV4xxDgDoyriCeXteODx+cpL7xzQHAHRl2JuMvGRVdXyS97fWdjx7/CtJNlbVqzN917E1o54DABaCke1xt9bWDr5+f6dob/8cfG2SW5Oc1Vp7YlRzAMBCMvI97tm01h7LC2eWAwBDcFIYAHREuAGgI8INAB0RbgDoiHADQEeEGwA6ItwA0BHhBoCOCDcAdES4AaAjwg0AHRFuAOiIcANAR4QbADoi3ADQEeEGgI4sGfcAwO6tX78+U1NTOfzww3PZZZeNexxgjIQbOjA1NZXJyclxjwHsAxwqB4COCDcAdMShcthDp3zs6nl/z1c+/LPsl+SBh382r+9/+6fWzdt7AcOxxw0AHRFuAOiIcANAR3zGDR14fv/lv/QVWLyEGzqwbdVbxz0CsI9wqBwAOiLcANAR4QaAjgg3AHREuAGgI8INAB0RbgDoiHADQEeEGwA6ItwA0BHhBoCOCDcAdES4AaAjwg0AHRFuAOiIcANAR4QbADoi3ADQEeEGgI4INwB0RLgBoCPCDQAdEW4A6IhwA0BHhBsAOiLcANAR4QaAjgg3AHREuAGgI8INAB0RbgDoiHADQEeEGwA6ItwA0BHhBoCOCDcAdES4AaAjIwl3VV1ZVbdU1YWz/P2SqnqgqiYGf04cxRwAsNDMebir6pwk+7XWTktybFWtmmHZSUmuba2tHfz53lzPAQAL0Sj2uNcm+fLg8Q1JTp9hzZok76yq7w72zpeMYA4AWHBGEe7lSSYHjx9NctgMa25L8pbW2qlJliZ5x84LquqCqtpUVZu2bNkygjEBoD+jCPeTSV4+eLxilve4q7X24ODxpiQvOpzeWtvQWlvdWlt96KGHjmBMAOjPKMJ9e144PH5ykvtnWHNNVZ1cVfsleXeSO0cwB/Ng/fr1WbduXdavXz/uUQAWhVF8tvyVJBur6tVJ3p7kfVV1SWttxzPMP5Hkr5JUkq+21r45gjmYB1NTU5mcnNz9QgDmxJyHu7W2tarWJjk7yWWttanstEfdWrs702eWAwB7YCRnc7fWHssLZ5YDAHPEldMAoCN+f3qBeeAT83sRumcfPSjJkjz76I/n/b2Push1e4DFxx43AHREuAGgI8INAB0RbgDoiJPT2CuHLHs+ybODrwCMmnCzV/7gpMfHPQLAouJQOQB0RLgBoCPCDQAdEW4A6IhwA0BHhBsAOiLcANAR4QaAjgg3AHREuAGgI8INAB0RbgDoiHADQEeEGwA6ItwA0BHhBoCOCDcAdES4AaAjwg0AHRFuAOiIcANAR4QbADoi3ADQEeEGgI4INwB0RLgBoCNLxj0AwI7Wr1+fqampHH744bnsssvGPQ7sc4Qb2KdMTU1lcnJy3GPAPsuhcgDoiD1uYFYPfOLEeX/PZx89KMmSPPvoj+f9/Y+66Hvz+n7wUtjjBoCO2OMG9imHLHs+ybODr8DOhBvYp/zBSY+PewTYpzlUDgAdEW4A6IhwA0BHhBsAOiLcANAR4QaAjgg3AHREuAGgI8INAB0RbgDoiHADQEeEGwA6ItwA0BHhBoCOCDcAdES4AaAjwg0AHRFuAOiIcANAR4QbADoi3ADQEeEGgI6MJNxVdWVV3VJVF+7NGgDgl815uKvqnCT7tdZOS3JsVa16KWsAYC6sX78+69aty/r168c9ypxYMoJtrk3y5cHjG5KcnuS+l7AGYFE45WNXj3uEeXP7p9bN+3tOTU1lcnJy3t93VEYR7uVJtv8v9GiSN72UNVV1QZILBt8+WVX3zvGcC9KvJYckeXjcc8yL/1DjnmDBW1Q/T4mfqXlQf/zBsf5MXXPNNeN665fi12Z6chThfjLJywePV2Tmw/G7XdNa25BkwwjmW9CqalNrbfW452Bh8PPEXPMztfdGcXLa7Zk+9J0kJye5/yWuAQB2Moo97q8k2VhVr07y9iTvq6pLWmsX7mLNmhHMAQALzpzvcbfWtmb65LNbk5zVWrtzp2jPtOaJuZ5jEfPxAnPJzxNzzc/UXqrW2rhnAACG5MppANAR4QZmVVWHVdXGcc/BvqeqPl9Vpw8eX1xV/2KGNRdX1dr5nm2hE+4FwiVkmWtVdWCSL2T6ugvAPkK4FwCXkGVEnkvy3iRbxz0IXfh8VV1UVRur6ttVtf1aHamqs6rqv1XVkqo6qKr+ZrDuTwfP3VJVv1lV362q06rqs4M9+hm3t9gJ98KwNi++hCzsldbaVr/xwR5a0Vo7I8k9Sd44eO4NSS5L8v7W2rNJ/n2SLw7WHZDkLUl+nuSkJA8kOTHJHbvY3qIn3AvDzpeQPWyMswCLw86/ktQy/dFKMh3g/QePfz/JE0kOHnx/fJLvDB5/J8nrk/wgyTuSTCT57UxfpCuzbG/RE+6FYZjLzALMpYeSHDt4fGySqSTbZlj3b5J8PMkfDb7/X3nholtrBt//bZLXDR6fleTuwd/PtL1Fz//BLwwuIQvMt8uT/KvBbx0sS3LjLOuebq19J8kBVfXrST6Z6Stq3pzk8dbaDZk+NP5/k/zvJPe01p4Z+fQdcwGWBaCqVibZmOR/ZHAJWZ9NAixMwr1ADH515+wkN7XWpsY9DwCjIdwA0BGfcQNAR4QbADoi3ADQEeGGRcRNH6B/wg0AHVky7gGA4QxusnBdkoOS/J9MX13qTUl+Jcn3Wmu/X1UXJ1ma5IwkK5O8LcnfJ/nrJPslqSQTVfWKJFfv+NrBe0wkuS3JSa213xp2jtbapTu/tqpeluTzSV6dZHOS3830daonWmsTVXX+YJNHJ/nNJK9IsiXJ+wbXtAZmYI8b+vG6TAfw9CSvTfL/Mh3NM5P8alWdNFj32sFz1yV5c5ILknyttXZWku1XpLpglteuSXLLbNGeaY7W2qWzvPbDg/f4x0nuS/Ivd7HNjYN1DyV51+7+h4DFTLihH5NJTklyU5I/S3Jckt8Z7Okem+SIwbqrB1+335jhmCR3Dp7bNPg622vvbq1dt4dzbLfza3e8mcStmb6ZxI52vE3j9ptK3JXpPXBgFsIN/Xhbkj9qrZ3WWvtPSe5N8qettbVJLsx0qJMX35jhgUzfWjFJfn3wdbbXPvkS5thu59fOdDOJnyc5dIftbHfq4Osbk/xwiBlg0RJu6MffJvl0VX2rqr6Y6Zs6vL2qbkrye0n+bpbXbUhy7mDveuXguT8f8rW7naOqTphl3RVJ3jB4j1WZ/rz7q0k+UlWXJ3lkh7W/MZjvVUm+tgezwKLjkqfQiar6cJJ/lunPqZ9J8settYne5xicUDcxjn8L9Ei4gVkN9oJ39ERrzcljMEbCDQAd8Rk3AHREuAGgI8INAB0RbgDoiHADQEf+P/htZ169SK5PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1.不同性别的顾客，线上、线下方式的对比\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.barplot(x='gender_group',y= 'revenue',hue ='channel',data = data1,\n",
    "           estimator =sum,\n",
    "           order = data1.groupby('gender_group').revenue.sum().sort_values(ascending=False).index\n",
    "           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f3635288",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age_group', ylabel='revenue'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2.不同年龄段的顾客，线上、线下方式的对比\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.barplot(x='age_group',y= 'revenue',hue ='channel',data = data1,\n",
    "           estimator =sum,\n",
    "           order =data1.groupby('age_group').revenue.sum().sort_values(ascending=False).index\n",
    "           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b22353d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='product', ylabel='margin'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 新增'利润'字段\n",
    "data1['margin']= data1.unit_price - data1.unit_cost\n",
    "data1.head()\n",
    "# 注意：新增一列的写法，不能写成uniqlo.margin\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.barplot(x='product',y= 'margin',data = data1,\n",
    "           order =data1.groupby('product').margin.mean().sort_values(ascending=False).index\n",
    "           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ec8f57ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='product', ylabel='margin'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 不同产品的利润分布\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.unicode_minus']=False # 纵轴无法显示负号问题\n",
    "plt.figure(figsize=(8,10))\n",
    "sns.boxplot(x='product',y='margin',data=data1,\n",
    "           order =data1.groupby('product').margin.mean().sort_values(ascending=False).index\n",
    "           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c1dd3d06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>margin</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>unit_cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>margin</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.911749</td>\n",
       "      <td>0.102540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unit_price</th>\n",
       "      <td>0.911749</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.502075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unit_cost</th>\n",
       "      <td>0.102540</td>\n",
       "      <td>0.502075</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              margin  unit_price  unit_cost\n",
       "margin      1.000000    0.911749   0.102540\n",
       "unit_price  0.911749    1.000000   0.502075\n",
       "unit_cost   0.102540    0.502075   1.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算单价销售额和单件产品成本之间的相关性\n",
    "q = ['margin','unit_price','unit_cost'] #列选择\n",
    "data1[q].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3da68b15",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 热力图\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.heatmap(data1[q].corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4a8941ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>channel</th>\n",
       "      <th>gender_group</th>\n",
       "      <th>age_group</th>\n",
       "      <th>wkd_ind</th>\n",
       "      <th>product</th>\n",
       "      <th>customer</th>\n",
       "      <th>revenue</th>\n",
       "      <th>order</th>\n",
       "      <th>quant</th>\n",
       "      <th>unit_cost</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>margin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1154</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>181</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>432</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>132</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>501</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>287</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>86</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>462</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>460</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>41</td>\n",
       "      <td>-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>557</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>181</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   city  channel  gender_group  age_group  wkd_ind  product  customer  \\\n",
       "0     7        1             0          1        0        1         3   \n",
       "1     5        1             0          1        0        7         0   \n",
       "2     7        1             1          9        0        0         1   \n",
       "3     7        1             0          1        0        0         0   \n",
       "4     7        1             1          0        1        5         1   \n",
       "5     6        0             0          3        1        0         0   \n",
       "6     5        1             0          1        1        4         0   \n",
       "7     5        1             1          9        1        0         1   \n",
       "8     5        1             0          2        1        3         2   \n",
       "9     1        1             0          5        1        2         0   \n",
       "\n",
       "   revenue  order  quant  unit_cost  unit_price  margin  \n",
       "0     1154      3      3          4         181     140  \n",
       "1      432      0      0          3         132     100  \n",
       "2      501      1      1          3          78      40  \n",
       "3      152      0      0          3          48      10  \n",
       "4      163      1      2          0          11      13  \n",
       "5      287      0      0          3          86      48  \n",
       "6       56      0      0          1          22      14  \n",
       "7      462      1      1          3          68      30  \n",
       "8      460      2      2          5          41     -17  \n",
       "9      557      0      0          6         181     100  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore') # 忽略报错(警告)\n",
    "data2=data1.copy()\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "# 使用LabelEncoder编码——按照数据的首字母或者数字依次排序并依次赋予编码0,1,2……\n",
    "for i in ['channel','gender_group','age_group','wkd_ind','product','customer','revenue','order','quant','unit_cost','unit_price']:\n",
    "    data1[i] = LabelEncoder().fit_transform(data1[i])\n",
    "# 对于GenHealth列，如果利用LabelEncoder编码，就不是我们想要的按照Excellent - Yery good - Good - Fair - Poor编码为4,3,2,1,0；所以干脆替换\n",
    "data1.loc[:,'city'] = data1.loc[:,'city'].replace(['上海','北京','南京','广州','成都','杭州','武汉','深圳','西安','重庆'],[0,1,2,3,4,5,6,7,8,9])\n",
    "data1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "df631420",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>channel</th>\n",
       "      <th>gender_group</th>\n",
       "      <th>age_group</th>\n",
       "      <th>wkd_ind</th>\n",
       "      <th>product</th>\n",
       "      <th>customer</th>\n",
       "      <th>revenue</th>\n",
       "      <th>order</th>\n",
       "      <th>quant</th>\n",
       "      <th>unit_cost</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>margin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.818440</td>\n",
       "      <td>0.081081</td>\n",
       "      <td>0.068182</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.857820</td>\n",
       "      <td>0.647696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.306383</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.625592</td>\n",
       "      <td>0.539295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.355319</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.022727</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.369668</td>\n",
       "      <td>0.376694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.107801</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.227488</td>\n",
       "      <td>0.295393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.115603</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052133</td>\n",
       "      <td>0.303523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   city  channel  gender_group  age_group  wkd_ind  product  customer  \\\n",
       "0     7        1             0          1        0        1         3   \n",
       "1     5        1             0          1        0        7         0   \n",
       "2     7        1             1          9        0        0         1   \n",
       "3     7        1             0          1        0        0         0   \n",
       "4     7        1             1          0        1        5         1   \n",
       "\n",
       "    revenue     order     quant  unit_cost  unit_price    margin  \n",
       "0  0.818440  0.081081  0.068182   0.666667    0.857820  0.647696  \n",
       "1  0.306383  0.000000  0.000000   0.500000    0.625592  0.539295  \n",
       "2  0.355319  0.027027  0.022727   0.500000    0.369668  0.376694  \n",
       "3  0.107801  0.000000  0.000000   0.500000    0.227488  0.295393  \n",
       "4  0.115603  0.027027  0.045455   0.000000    0.052133  0.303523  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 采用归一化将数据缩放到0-1之间\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "data2_copy_KNN_1 = data1.copy()\n",
    "column_lst=['revenue','order','quant','unit_cost','unit_price','margin']\n",
    "for i in column_lst:\n",
    "    data2_copy_KNN_1[i]=MinMaxScaler().fit_transform(data2_copy_KNN_1[i].values.reshape(-1,1)).reshape(1,-1)[0]\n",
    "data2_copy_KNN_1.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cfe6e4d3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1a48a0f7970>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 581x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "sns.pairplot(pd.concat([data2_copy_KNN_1['city'],data2_copy_KNN_1['quant'],data2_copy_KNN_1['unit_cost'],data2_copy_KNN_1['margin']],axis=1),hue='city')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c3339aa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1a490c512b0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 583x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "sns.pairplot(pd.concat([data2_copy_KNN_1['city'],data2_copy_KNN_1['channel'],\n",
    "                        data2_copy_KNN_1['unit_cost'],data2_copy_KNN_1['margin']],axis=1),hue='channel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "eff72192",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16719, 12)\n",
      "(5574, 12)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>gender_group</th>\n",
       "      <th>age_group</th>\n",
       "      <th>wkd_ind</th>\n",
       "      <th>product</th>\n",
       "      <th>customer</th>\n",
       "      <th>revenue</th>\n",
       "      <th>order</th>\n",
       "      <th>quant</th>\n",
       "      <th>unit_cost</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>margin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13738</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.173050</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.322275</td>\n",
       "      <td>0.349593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12613</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.049645</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.132701</td>\n",
       "      <td>0.241192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10796</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.073050</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.022727</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.075829</td>\n",
       "      <td>0.317073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  gender_group  age_group  wkd_ind  product  customer   revenue  \\\n",
       "13738     7             0          3        1        0         0  0.173050   \n",
       "12613     7             1          4        0        0         0  0.049645   \n",
       "10796     7             0          2        0        5         1  0.073050   \n",
       "\n",
       "          order     quant  unit_cost  unit_price    margin  \n",
       "13738  0.000000  0.000000        0.5    0.322275  0.349593  \n",
       "12613  0.000000  0.000000        0.5    0.132701  0.241192  \n",
       "10796  0.027027  0.022727        0.0    0.075829  0.317073  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 切分data2_copy_KNN_1为数据和标签（也就是自变量和因变量）\n",
    "X = data2_copy_KNN_1.drop(columns=['channel'])\n",
    "y = data2_copy_KNN_1['channel']\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "X_train[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5b7db2cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82.43631144599928 83.62038033728024\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn_1 = KNeighborsClassifier()\n",
    "knn_1.fit(X_train, y_train)\n",
    "knn_1.score(X_test, y_test)*100\n",
    "knn_2 = KNeighborsClassifier(n_neighbors = 12) # 定义K=12\n",
    "knn_2.fit(X_train, y_train)\n",
    "knn_2.score(X_test, y_test)*100\n",
    "print(knn_1.score(X_test, y_test)*100,knn_2.score(X_test, y_test)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6a4fb8d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.23430211697166"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_3 = KNeighborsClassifier(weights=\"distance\")\n",
    "knn_3.fit(X_train, y_train)\n",
    "knn_3.score(X_test, y_test)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "52e0dae4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16719, 4) (5574, 4)\n"
     ]
    }
   ],
   "source": [
    "X_train_1 = pd.concat([X_train['city'],\n",
    "                       X_train['gender_group'],\n",
    "                       X_train['age_group'],\n",
    "                       X_train['wkd_ind']],axis=1)\n",
    "X_test_1 = pd.concat([X_test['city'],\n",
    "                      X_test['gender_group'],\n",
    "                      X_test['age_group'],\n",
    "                      X_test['wkd_ind']],axis=1)\n",
    "# y_train, y_test仍然和原来是一样的\n",
    "print(X_train_1.shape, X_test_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "66c2af77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.38715464657338"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_4 = KNeighborsClassifier()\n",
    "knn_4.fit(X_train_1, y_train)\n",
    "knn_4.score(X_test_1, y_test)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1343e75b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8338715464657338"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = knn_4.predict(X_test_1)\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c0323298",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "channel\n",
       "0     3890\n",
       "1    18403\n",
       "Name: channel, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2_copy_KNN_1['channel'].groupby(data2_copy_KNN_1['channel']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b52ae8fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8255057641412102"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2_copy_KNN_1['channel'].groupby(data2_copy_KNN_1['channel']).count()[1] \n",
    "/ (data2_copy_KNN_1['channel'].groupby(data2_copy_KNN_1['channel']).count()[0]\n",
    "   +data2_copy_KNN_1['channel'].groupby(data2_copy_KNN_1['channel']).count()[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ac40175",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8b8aac0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94f341aa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ae93aad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68819505",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19b83e17",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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